Also, rare experiences can be forgotten rapidly because of the way \(Q\)  values are updated. Such experiences are useful because if the sampled experiences are dominated by those frequently occurring, the Double DQN can become biased towards selecting actions useful only for those experiences. PER breaks this correlation and avoids large oscillations of the network parameters leading to more stable learning, while allowing experiences to be seen more often, hence, also avoiding rare experiences from being forgotten fast [1]}.
